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Get Started Free →Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.
.claude/skills/kxiandaoyan-pdf/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 76% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 332% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 179% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 39% | 0% |
This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see reference.md. If you need to fill out a PDF form, read forms.md and follow its instructions.
pythonfrom pypdf import PdfReader, PdfWriter # Read a PDF reader = PdfReader("document.pdf") print(f"Pages: {len(reader.pages)}") # Extract text text = "" for page in reader.pages: text += page.extract_text()
pythonfrom pypdf import PdfWriter, PdfReader writer = PdfWriter() for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]: reader = PdfReader(pdf_file) for page in reader.pages: writer.add_page(page) with open("merged.pdf", "wb") as output: writer.write(output)
pythonreader = PdfReader("input.pdf") for i, page in enumerate(reader.pages): writer = PdfWriter() writer.add_page(page) with open(f"page_{i+1}.pdf", "wb") as output: writer.write(output)
pythonreader = PdfReader("document.pdf") meta = reader.metadata print(f"Title: {meta.title}") print(f"Author: {meta.author}") print(f"Subject: {meta.subject}") print(f"Creator: {meta.creator}")
pythonreader = PdfReader("input.pdf") writer = PdfWriter() page = reader.pages[0] page.rotate(90) # Rotate 90 degrees clockwise writer.add_page(page) with open("rotated.pdf", "wb") as output: writer.write(output)
pythonimport pdfplumber with pdfplumber.open("document.pdf") as pdf: for page in pdf.pages: text = page.extract_text() print(text)
pythonwith pdfplumber.open("document.pdf") as pdf: for i, page in enumerate(pdf.pages): tables = page.extract_tables() for j, table in enumerate(tables): print(f"Table {j+1} on page {i+1}:") for row in table: print(row)
pythonimport pandas as pd with pdfplumber.open("document.pdf") as pdf: all_tables = [] for page in pdf.pages: tables = page.extract_tables() for table in tables: if table: # Check if table is not empty df = pd.DataFrame(table[1:], columns=table[0]) all_tables.append(df) # Combine all tables if all_tables: combined_df = pd.concat(all_tables, ignore_index=True) combined_df.to_excel("extracted_tables.xlsx", index=False)
pythonfrom reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas c = canvas.Canvas("hello.pdf", pagesize=letter) width, height = letter # Add text c.drawString(100, height - 100, "Hello World!") c.drawString(100, height - 120, "This is a PDF created with reportlab") # Add a line c.line(100, height - 140, 400, height - 140) # Save c.save()
pythonfrom reportlab.lib.pagesizes import letter from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak from reportlab.lib.styles import getSampleStyleSheet doc = SimpleDocTemplate("report.pdf", pagesize=letter) styles = getSampleStyleSheet() story = [] # Add content title = Paragraph("Report Title", styles['Title']) story.append(title) story.append(Spacer(1, 12)) body = Paragraph("This is the body of the report. " * 20, styles['Normal']) story.append(body) story.append(PageBreak()) # Page 2 story.append(Paragraph("Page 2", styles['Heading1'])) story.append(Paragraph("Content for page 2", styles['Normal'])) # Build PDF doc.build(story)
bash# Extract text pdftotext input.pdf output.txt # Extract text preserving layout pdftotext -layout input.pdf output.txt # Extract specific pages pdftotext -f 1 -l 5 input.pdf output.txt # Pages 1-5
bash# Merge PDFs qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf # Split pages qpdf input.pdf --pages . 1-5 -- pages1-5.pdf qpdf input.pdf --pages . 6-10 -- pages6-10.pdf # Rotate pages qpdf input.pdf output.pdf --rotate=+90:1 # Rotate page 1 by 90 degrees # Remove password qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf
bash# Merge pdftk file1.pdf file2.pdf cat output merged.pdf # Split pdftk input.pdf burst # Rotate pdftk input.pdf rotate 1east output rotated.pdf
python# Requires: pip install pytesseract pdf2image import pytesseract from pdf2image import convert_from_path # Convert PDF to images images = convert_from_path('scanned.pdf') # OCR each page text = "" for i, image in enumerate(images): text += f"Page {i+1}:\n" text += pytesseract.image_to_string(image) text += "\n\n" print(text)
pythonfrom pypdf import PdfReader, PdfWriter # Create watermark (or load existing) watermark = PdfReader("watermark.pdf").pages[0] # Apply to all pages reader = PdfReader("document.pdf") writer = PdfWriter() for page in reader.pages: page.merge_page(watermark) writer.add_page(page) with open("watermarked.pdf", "wb") as output: writer.write(output)
bash# Using pdfimages (poppler-utils) pdfimages -j input.pdf output_prefix # This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.
pythonfrom pypdf import PdfReader, PdfWriter reader = PdfReader("input.pdf") writer = PdfWriter() for page in reader.pages: writer.add_page(page) # Add password writer.encrypt("userpassword", "ownerpassword") with open("encrypted.pdf", "wb") as output: writer.write(output)
The container has Noto CJK fonts pre-installed. When creating PDFs with Chinese, Japanese, or Korean text using reportlab, you must register the CJK font explicitly:
pythonfrom reportlab.pdfbase import pdfmetrics from reportlab.pdfbase.ttfonts import TTFont from reportlab.lib.pagesizes import A4 from reportlab.pdfgen import canvas # Register Noto Sans CJK font (pre-installed in container) pdfmetrics.registerFont(TTFont('NotoSansCJK', '/usr/share/fonts/noto/NotoSansCJK-Regular.ttc')) c = canvas.Canvas("/shared/output.pdf", pagesize=A4) c.setFont('NotoSansCJK', 14) c.drawString(72, 750, "你好世界 Hello World こんにちは 안녕하세요") c.save()
For Platypus (SimpleDocTemplate), register the font and create a custom style:
pythonfrom reportlab.lib.styles import ParagraphStyle from reportlab.platypus import SimpleDocTemplate, Paragraph style_cn = ParagraphStyle('Chinese', fontName='NotoSansCJK', fontSize=12, leading=16) story = [Paragraph("中文内容示例", style_cn)] doc = SimpleDocTemplate("/shared/report.pdf") doc.build(story)
> Important: Always use the registered CJK font name (NotoSansCJK) for any text containing CJK characters. The default reportlab fonts do NOT support CJK and will produce garbled output.
| Task | Best Tool | Command/Code | |------|-----------|--------------| | Merge PDFs | pypdf | writer.add_page(page) | | Split PDFs | pypdf | One page per file | | Extract text | pdfplumber | page.extract_text() | | Extract tables | pdfplumber | page.extract_tables() | | Create PDFs | reportlab | Canvas or Platypus | | Command line merge | qpdf | qpdf --empty --pages ... | | OCR scanned PDFs | pytesseract | Convert to image first | | Fill PDF forms | pdf-lib or pypdf (see forms.md) | See forms.md |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,851 | 18,373 | +3% | 1 | 1 | 0% | 3,462 | 6,086 | +76% | 0 | 0 | — |
case-02 | fail→pass | 11,991 | 3,575 | -70% | 1 | 1 | 0% | 2,320 | 3,068 | +32% | 0 | 0 | — |
case-03 | pass→pass | 13,255 | 5,509 | -58% | 1 | 1 | 0% | 2,483 | 3,440 | +39% | 0 | 0 | — |
case-04 | pass→pass | 4,535 | 2,298 | -49% | 1 | 1 | 0% | 775 | 2,771 | +258% | 0 | 0 | — |
case-05 | pass→pass | 5,280 | 2,378 | -55% | 1 | 1 | 0% | 861 | 2,742 | +218% | 0 | 0 | — |
case-06 | fail→pass | 3,839 | 4,337 | +13% | 1 | 1 | 0% | 623 | 2,691 | +332% | 0 | 0 | — |
case-07 | pass→pass | 3,985 | 3,122 | -22% | 1 | 1 | 0% | 718 | 2,957 | +312% | 0 | 0 | — |
case-08 | pass→pass | 11,790 | 6,470 | -45% | 1 | 1 | 0% | 2,241 | 3,700 | +65% | 0 | 0 | — |
case-21 | pass→pass | 3,912 | 3,026 | -23% | 1 | 1 | 0% | 674 | 2,909 | +332% | 0 | 0 | — |
case-09 | pass→pass | 4,638 | 2,514 | -46% | 1 | 1 | 0% | 811 | 2,793 | +244% | 0 | 0 | — |
case-10 | pass→pass | 12,565 | 8,844 | -30% | 1 | 1 | 0% | 2,467 | 4,028 | +63% | 0 | 0 | — |
case-11 | fail→fail | 5,545 | 3,258 | -41% | 1 | 1 | 0% | 1,007 | 2,948 | +193% | 0 | 0 | — |
case-12 | pass→pass | 6,988 | 2,654 | -62% | 1 | 1 | 0% | 1,353 | 2,827 | +109% | 0 | 0 | — |
case-13 | pass→pass | 4,871 | 3,960 | -19% | 1 | 1 | 0% | 955 | 3,187 | +234% | 0 | 0 | — |
case-14 | pass→pass | 6,203 | 3,326 | -46% | 1 | 1 | 0% | 1,212 | 3,020 | +149% | 0 | 0 | — |
case-15 | pass→pass | 3,036 | 2,251 | -26% | 1 | 1 | 0% | 621 | 2,806 | +352% | 0 | 0 | — |
case-16 | pass→pass | 7,288 | 3,618 | -50% | 1 | 1 | 0% | 1,520 | 2,961 | +95% | 0 | 0 | — |
case-17 | pass→pass | 2,496 | 1,592 | -36% | 1 | 1 | 0% | 440 | 2,616 | +495% | 0 | 0 | — |
case-18 | pass→pass | 4,070 | 2,463 | -39% | 1 | 1 | 0% | 701 | 2,737 | +290% | 0 | 0 | — |
case-19 | fail→pass | 6,242 | 4,377 | -30% | 1 | 1 | 0% | 1,139 | 3,181 | +179% | 0 | 0 | — |
case-20 | pass→pass | 2,528 | 2,620 | +4% | 1 | 1 | 0% | 401 | 2,765 | +590% | 0 | 0 | — |
case-22 | pass→pass | 5,282 | 5,056 | -4% | 1 | 1 | 0% | 891 | 3,320 | +273% | 0 | 0 | — |
case-23 | pass→pass | 4,071 | 4,539 | +11% | 1 | 1 | 0% | 665 | 3,175 | +377% | 0 | 0 | — |
case-24 | pass→pass | 5,255 | 2,419 | -54% | 1 | 1 | 0% | 930 | 2,810 | +202% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +17 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.